{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归的损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def costy1(x):\n",
    "    return -np.log(x)\n",
    "def costy0(x):\n",
    "    return -np.log(1-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x = np.linspace(0, 1, 50)\n",
    "\n",
    "plt.plot(x, costy1(x), label=\"y=1\")\n",
    "plt.plot(x, costy0(x), label=\"y=0\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
   "language": "python",
   "name": "python36864bitanacondaconda085db2e8b14649f4b32196068f7124e9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}